In data science courses, students write computer programs to help analyze large sets of data.

In data science courses, students write computer programs to help analyze large sets of data.

High school math, and algebra in particular, is in crisis. Although some students thrive on the numeracy path, most do not. Algebra I is the most failed course in American high schools. Thirty-three percent of students in California, for example, took Algebra I at least twice during high school. And students of color or those living in poverty are overrepresented in this group.

Some argue that algebra as part of the path to calculus is becoming increasingly irrelevant in today’s world and that students would be better served by taking fewer courses in algebra and more in areas such as statistics and data science. The University of California, for example, has decided that courses in statistics and data science can be taken instead of algebra 2 to meet its admission requirements.

Others oppose this approach, arguing that high-level participation in science, technology, engineering and math careers will ultimately require computation, and that attracting students away from Algebra 2 and towards Data Science will cut them off from these career opportunities. , including jobs in data science! In addition, many fear that students from disadvantaged backgrounds, who are at greater risk of failing Algebra I, are the most likely to be followed in these alternative math paths, and therefore more likely to be lost from the pipeline. STEM.

Students shouldn’t have to choose between more engaging, relevant, and modern math courses (the data science path) and ones that give them the opportunity to study the math they’ll need if they want to pursue STEM-related careers (the computational pathway). All students could benefit from learning about statistics, data science, and coding. But if they plan to work in data science or other STEM-related fields, they will also need a deep understanding of algebra.

Arguments about content to include in high school math ignore the elephant in the room: *O**We haven’t yet figured out how to properly teach algebra concepts to most students.*.

Many students who pass Algebra 1 do not master the content in depth enough to prepare them for Algebra 2, let alone higher-level STEM courses. And many students who do fairly well in algebra find it boring and irrelevant to their own lives. Students can’t be blamed for their lack of interest in learning the “steps” needed to solve X or silly acronyms like FOIL (a mnemonic to help students remember how to factor polynomials). This view of what algebra is cannot sustain most students’ motivation to pursue STEM-related careers.

Data science is not an alternative to algebra; in fact, it may very well be the key to understanding how to teach algebra well. High school algebra, in our view, desperately needs data science, a catch-all term for quantitative reasoning and the mathematical ideas that go into working with data collected in the real world. Data science has the potential to make algebra relevant and interesting for students who want to understand and improve the world. Data science is perhaps the best answer we have to the question most often asked by high school algebra students: how am I going to use it?

But just like algebra needs data science, data science needs algebra. The basic functions taught in high school algebra (eg, linear, polynomial, etc.) are used to model patterns in data. We have pursued this possibility by developing a statistics and data science curriculum for high school and junior college that emphasizes the fundamental concepts of algebra and data science: functions and patterning. Our students don’t learn functions as mathematical abstractions, but they use them as imperfect models that can help us understand and predict variations in the world.

While in math functions can make accurate predictions, in data science predictions are almost always wrong; the models always have an error. The reason this is true is that real data is always messier than the world of pure math. It is this mess, however, that attracts students. Finally, they see how features that bored them in theory can help them make better (even if not perfect) predictions in real-world settings. They learn that imperfect models are better than no model at all.

We teach our students linear functions. But they often ask if there are other functions to model more complex data patterns, such as curves. Teachers are excited that students want to learn about exponential, logarithmic, and polynomial functions, which many students were previously exposed to without realizing their value. Students are eager to learn algebra!

Imagine a world where students feel the need for algebraic functions rather than being pressured to learn them from so-called “mathematicians.” Imagine a generation of students who think that an exponential function could be useful for them to learn. If algebra can encompass data science and data science can do the same for algebra, we can all expect a world where students feel dissatisfied with their current knowledge and *want to* to learn more about math.

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**Ji Y. Son** is a professor of psychology at California State University, Los Angeles.** James W. Stigler** is a distinguished professor of psychology at UCLA. They study how students learn in complex subject areas and are co-founders of CourseKata.org, a statistics and data science program used by high schools and colleges.

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